This study aims to define the energy usage efficiency in apple cultivation in the Province of Tekirdağ. The study was conducted during 2015 production season through observation and measurement in an apple garden with a land area of 12 da and located in Nusratlı village in Central Tekirdağ. It has been tried to reveal the role of mechanization energy among all the inputs. According to the calculated data, in apple cultivation the respective figures for total energy input, total fruition, total energy output, energy output/input rate, specific energy, energy productivity and net energy have been calculated as 58839.65 MJ ha-1, 38370 kg ha-1, 92088.00 MJ ha-1, 1.56, 1.53 MJ kg-1, 0.65 kg MJ-1 and 33248.35 MJ ha-1 respectively. As a result, among the general energy inputs in apple cultivation, the highest energy consuming items have been respectively defined as fertilizer energy, fuel-oil energy, chemicals, machinery, human labour and irrigation energy.

A spraying machine was designed with this study. This machine can contuniously sets the amount of liquid chemical continuously depending on changes in leaf density, volume of the canopy, size and shapes which differs during growing season. These processes will optimise efficiency of spraying application. Ultrasonic sensors were used with this aim to define size of plant for adapting of chemical dosage octopus spraying machine was used at study. A system was designed for every arm’s of octopus machine. Three solenoid valve, one ultrasonic sensor, and electronic control unit which process and interpret the range data of ultrasonic sensor and also control solenoid valve, were added to arms. Flow rate of chemical can be set to three different value. Because every single electrovalve control different type of nozzle. Amounts of spraying chemical is set real-time by system depending on canopy volume and leaf density.

Self propelled agricultural vehicle prototype is developed for row-crop cultivation at this project. This robot is moving automaticly with legs . System can be programmed for different purposes and functions. These functions are transport, plant identification, weed detection. Basically, the robot, can go between rows, and can turn to next row at the end of the rows. The robot is working with the energy from the battery. The robot consists host computer, camera, laser scanner sensor, servo motors and arduino mega.The software at the host computer receive data from laser scanner and camera, makes all the calculations and decision-making process. In addition, some guidance can be done with voice commands . The robot is moving with legs instead of wheels. Walking of the robot is provided by sequential operations servo motors used in the legs. Servo motors are driving by arduino mega microprocessor and driver circuit. The robot can also be used with the help of an operator. In addition to the operator to be able to command in undesirable situations, robot can be controlled remotely with a joystick or voice commands.

Nowadays,increasing amount of energy use and limited energy sources are important problem for all over the world. Because of this there is an increasing concern about new and renewable energy source researchs. Especially solar energy is a great alternative to petroleum-based energy sources and it is nearly limitless.In this study a monocoque body design and manufacturing of this body was examined. In design, the limited energy obtained from the solar panels to be used in the most efficient way, manufacturing of the vehicle is made as light as possible with a good aerodynamic shape for minimizing aerodynamic drag resistance. For this purpose the lower and upper shell of the vehicle has been manufactured from fiberglass. This body was used for NKU Gunesinoglu Solar Car team, at FORMULA G 2014 which is organized by TUBITAK.

The aim of this research was to track the growth of chicken eggs, and make a decision as to whether the egg was fertilized or not. A digital imaging system has been developed in order to take an image from six different points without damaging the egg shell. All the images were transferred to a PC and turned into binary images. All the images were reduced to 1024 pixels and fed directly into the classification algorithm. The logistic regression method was used to discriminate the fertility of the eggs. Python programming language and the scikit-learn machine learning library was used to carry out the classifications. True positive, true negative, wrong positive, and wrong negative detection numbers in the trials were 350, 344, 56, and 50, respectively. Negative indicates the egg was infertile, and positive indicated that the egg was fertilized. The model accuracy was measured as 0.8675.

Spray drift can be defined as unwanted physical movement of spray droplets into non-target areas by air movements while application and after application (R. Frank, 1988). It can be divided into two main categories by type of occurrence.